Deep Unfolding [1]} aims to merge model-based and deep-learning based approaches for solving inverse problems (e.g., image restoration problems). The general idea is to design an iterative algorithm for the problem at hand and then convert certain steps of the iterative algorithm into learnable parameters.
In the seminal work [2]}, Gregor and LeCun proposed to convert the iterative shrinkage-thresholding algorithm (ISTA) into a deep network by setting the dictionaries in ISTA as learnable parameters. In [3]}, ADMM-Net has been proposed to unfold the Alternating Direction Method of Multipliers (ADMM) algorithm for compressive sensing Magnetic Resonance Imaging (MRI) reconstruction.
In [4]}, a deep unfolding network for single image super-resolution has been proposed by unfolding Maximum-a-Posteriori (MAP) formulation via a half-quadratic splitting algorithm and interpreting the prior term as a denoiser. A Deep Unrolling for Blind Deblurring (DUBLID) network [5]} unfolds a total variation based blind deconvolution algorithm and contains a very small number of learnable parameters.
In [6]}, Wang et al. proposed a model-inspired rain removal deep unfolding network based on proximal gradient descent to simplify computations. Recently, Pu et al. [7]} proposed a self-supervised deep unfolding network for separating X-Ray images of Artworks.
